Parapedobacter composti (DSM 22900, JCM 15978, KACC 10972)

Name: Parapedobacter composti (DSM 22900, JCM 15978, KACC 10972)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 14136

Type strain

Strain Designation 4M40

Culture col. no. DSM 22900 JCM 15978 KACC 10972 4M40

NCBI tax ID(s) 623281

Special Collections DSMZ JCM Literature strains

History DSMZ <- S.-W. Kwon, KACC; 4M40 <- H.-Y. Weon JCM <-- S.-W. Kwon 4M40.

Links

Parapedobacter composti 4M40 is an aerobe, Gram-negative, rod-shaped bacterium that was isolated from cotton waste compost.

Gram-negative rod-shaped aerobe genome sequence 16S sequence Bacteria

Name and taxonomic classification

SI-ID 402739

JCM 15978,DSM 22900

@ref 20215

Last LPSN update 2026-05-29 08:28:13

Domain Bacteria

Phylum Bacteroidota

Class Sphingobacteriia

Order Sphingobacteriales

Family Sphingobacteriaceae

Genus Parapedobacter

Species Parapedobacter composti

Full scientific name Parapedobacter composti Kim et al. 2010

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Motility	Confidence
29451	negative	1.2-5 µm	0.2-0.5 µm	rod-shaped
125438	negative					96.978

Pigmentation

29451

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
16611	R2A MEDIUM (DSMZ Medium 830)		Medium recipe at MediaDive	Name: R2A MEDIUM (DSMZ Medium 830) Composition: Agar 15.0 g/l Casamino acids 0.5 g/l Starch 0.5 g/l Glucose 0.5 g/l Proteose peptone 0.5 g/l Yeast extract 0.5 g/l K2HPO4 0.3 g/l Na-pyruvate 0.3 g/l MgSO4 x 7 H2O 0.05 g/l Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)
16611	positive	growth	28
29451	positive	growth	15-45
29451	positive	optimum	30
67770	positive	growth	30

Culture pH

@ref	Ability	Type	PH
29451	positive	optimum	7
29451	positive	growth	06-08

Physiology and metabolism

Oxygen tolerance

29451

Oxygen toleranceaerobe

Spore formation

@ref	Spore formation	Confidence
29451
125439		96.928

Halophily

@ref	Salt	Growth	Tested relation	Concentration
29451	NaCl	positive	growth	0-5 %

Observation

@ref	Observation
29451	aggregates in chains
67770	quinones: MK-7

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29451	22599 ChEBI	arabinose	+	carbon source
29451	17234 ChEBI	glucose	+	carbon source
29451	28087 ChEBI	glycogen	+	carbon source
29451	37684 ChEBI	mannose	+	carbon source
29451	28053 ChEBI	melibiose	+	carbon source
29451	26546 ChEBI	rhamnose	+	carbon source
29451	33942 ChEBI	ribose	+	carbon source
29451	17814 ChEBI	salicin	+	carbon source
29451	17992 ChEBI	sucrose	+	carbon source

Enzymes

@ref	Value	Activity	Ec
29451	catalase	+	1.11.1.6
29451	cytochrome oxidase	+	1.9.3.1

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	cellulose degradation	100	5 of 5
66794	gluconeogenesis	100	8 of 8
66794	palmitate biosynthesis	100	22 of 22
66794	coenzyme A metabolism	100	4 of 4
66794	formaldehyde oxidation	100	3 of 3
66794	glycogen metabolism	100	5 of 5
66794	starch degradation	100	10 of 10
66794	adipate degradation	100	2 of 2
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	ppGpp biosynthesis	100	4 of 4
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	folate polyglutamylation	100	1 of 1
66794	sulfopterin metabolism	100	4 of 4
66794	methylglyoxal degradation	100	5 of 5
66794	suberin monomers biosynthesis	100	2 of 2
66794	vitamin B1 metabolism	92.31	12 of 13
66794	Entner Doudoroff pathway	90	9 of 10
66794	chorismate metabolism	88.89	8 of 9
66794	lipid A biosynthesis	88.89	8 of 9
66794	NAD metabolism	88.89	16 of 18
66794	C4 and CAM-carbon fixation	87.5	7 of 8
66794	tetrahydrofolate metabolism	85.71	12 of 14
66794	ubiquinone biosynthesis	85.71	6 of 7
66794	heme metabolism	85.71	12 of 14
66794	photosynthesis	85.71	12 of 14
66794	1,4-dihydroxy-6-naphthoate biosynthesis	83.33	5 of 6
66794	alanine metabolism	82.76	24 of 29
66794	pentose phosphate pathway	81.82	9 of 11
66794	proline metabolism	81.82	9 of 11
66794	degradation of sugar acids	80	20 of 25
66794	threonine metabolism	80	8 of 10
66794	metabolism of amino sugars and derivatives	80	4 of 5
66794	peptidoglycan biosynthesis	80	12 of 15
66794	aspartate and asparagine metabolism	77.78	7 of 9
66794	valine metabolism	77.78	7 of 9
66794	d-mannose degradation	77.78	7 of 9
66794	serine metabolism	77.78	7 of 9
66794	phenylalanine metabolism	76.92	10 of 13
66794	leucine metabolism	76.92	10 of 13
66794	purine metabolism	76.6	72 of 94
66794	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	75	6 of 8
66794	isoleucine metabolism	75	6 of 8
66794	glycogen biosynthesis	75	3 of 4
66794	acetate fermentation	75	3 of 4
66794	flavin biosynthesis	73.33	11 of 15
66794	metabolism of disaccharids	72.73	8 of 11
66794	citric acid cycle	71.43	10 of 14
66794	reductive acetyl coenzyme A pathway	71.43	5 of 7
66794	propanol degradation	71.43	5 of 7
66794	pyrimidine metabolism	71.11	32 of 45
66794	propionate fermentation	70	7 of 10
66794	4-hydroxyphenylacetate degradation	70	7 of 10
66794	isoprenoid biosynthesis	69.23	18 of 26
66794	methionine metabolism	69.23	18 of 26
66794	glutamate and glutamine metabolism	67.86	19 of 28
66794	cysteine metabolism	66.67	12 of 18
66794	UDP-GlcNAc biosynthesis	66.67	2 of 3
66794	degradation of hexoses	66.67	12 of 18
66794	L-lactaldehyde degradation	66.67	2 of 3
66794	CO2 fixation in Crenarchaeota	66.67	6 of 9
66794	acetoin degradation	66.67	2 of 3
66794	glycolysis	64.71	11 of 17
66794	degradation of pentoses	64.29	18 of 28
66794	glutathione metabolism	64.29	9 of 14
66794	vitamin B6 metabolism	63.64	7 of 11
66794	d-xylose degradation	63.64	7 of 11
66794	non-pathway related	63.16	24 of 38
66794	ketogluconate metabolism	62.5	5 of 8
66794	degradation of sugar alcohols	62.5	10 of 16
66794	dTDPLrhamnose biosynthesis	62.5	5 of 8
66794	histidine metabolism	62.07	18 of 29
66794	phenylacetate degradation (aerobic)	60	3 of 5
66794	vitamin K metabolism	60	3 of 5
66794	coenzyme M biosynthesis	60	6 of 10
66794	arachidonate biosynthesis	60	3 of 5
66794	glycine betaine biosynthesis	60	3 of 5
66794	lysine metabolism	59.52	25 of 42
66794	lipid metabolism	58.06	18 of 31
66794	tryptophan metabolism	55.26	21 of 38
66794	arginine metabolism	54.17	13 of 24
66794	urea cycle	53.85	7 of 13
66794	kanosamine biosynthesis II	50	1 of 2
66794	pantothenate biosynthesis	50	3 of 6
66794	glycolate and glyoxylate degradation	50	3 of 6
66794	degradation of aromatic, nitrogen containing compounds	50	6 of 12
66794	dolichol and dolichyl phosphate biosynthesis	50	1 of 2
66794	cyclohexanol degradation	50	2 of 4
66794	lactate fermentation	50	2 of 4
66794	quinate degradation	50	1 of 2
66794	tyrosine metabolism	50	7 of 14
66794	ethanol fermentation	50	1 of 2
66794	CMP-KDO biosynthesis	50	2 of 4
66794	ascorbate metabolism	45.45	10 of 22
66794	oxidative phosphorylation	45.05	41 of 91
66794	cardiolipin biosynthesis	42.86	3 of 7
66794	gallate degradation	40	2 of 5
66794	factor 420 biosynthesis	40	2 of 5
66794	3-chlorocatechol degradation	40	2 of 5
66794	lipoate biosynthesis	40	2 of 5
66794	phenol degradation	40	8 of 20
66794	glycine metabolism	40	4 of 10
66794	3-phenylpropionate degradation	40	6 of 15
66794	D-cycloserine biosynthesis	40	2 of 5
66794	bacilysin biosynthesis	40	2 of 5
66794	ethylmalonyl-CoA pathway	40	2 of 5
66794	myo-inositol biosynthesis	40	4 of 10
66794	arachidonic acid metabolism	38.89	7 of 18
66794	sulfate reduction	38.46	5 of 13
66794	androgen and estrogen metabolism	37.5	6 of 16
66794	cyanate degradation	33.33	1 of 3
66794	4-hydroxymandelate degradation	33.33	3 of 9
66794	(5R)-carbapenem carboxylate biosynthesis	33.33	1 of 3
66794	acetyl CoA biosynthesis	33.33	1 of 3
66794	octane oxidation	33.33	1 of 3
66794	selenocysteine biosynthesis	33.33	2 of 6
66794	IAA biosynthesis	33.33	1 of 3
66794	polyamine pathway	26.09	6 of 23
66794	butanoate fermentation	25	1 of 4
66794	catecholamine biosynthesis	25	1 of 4
66794	methanogenesis from CO2	25	3 of 12
66794	biotin biosynthesis	25	1 of 4
66794	toluene degradation	25	1 of 4
66794	phosphatidylethanolamine bioynthesis	23.08	3 of 13
66794	allantoin degradation	22.22	2 of 9

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Engineered	#Agriculture	-
#Engineered	#Biodegradation	#Composting
#Engineered	#Waste	#Solid plant waste
#Host	#Plants	#Shrub (Scrub)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent
16611	cotton waste compost	Suwon	Republic of Korea	KOR	Asia
67770	Cotton waste compost		Republic of Korea	KOR	Asia

Taxonmaps

69479

Global distribution of 16S sequence FJ754321 (>99% sequence identity) for Parapedobacter composti subclade from Microbeatlas

Aquatic Samples	0
Soil Samples	8
Animal Samples	5
Plant Samples	1
Total Samples	14

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
16611	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	IMG-taxon 2675903219 annotated assembly for Parapedobacter composti DSM 22900	contig	GCA_900112315	623281.3	2675903219	623281	66.76

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
16611	Parapedobacter composti strain 4M40 16S ribosomal RNA gene, partial sequence	FJ754321	1463		623281

GC content

@ref	GC-content (mol%)	Method
16611	48.6	high performance liquid chromatography (HPLC)
29451	48.6

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: IMG-taxon 2675903219 annotated assembly for Parapedobacter composti DSM 22900
NCBI: GCA_900112315

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	oxygen_tolerance	BacteriaNetⓘ	obligate anaerobe	84.90	no
125439	motility	BacteriaNetⓘ	no	65.54	no
125439	gram_stain	BacteriaNetⓘ	negative	81.75	no
125439	spore_formation	BacteriaNetⓘ	no	96.93	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Parapedobacter composti strain DSM 22900
PATRIC: 623281.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	96.98	yes
125438	anaerobic	anaerobicⓘ	no	95.23	yes
125438	spore-forming	spore-formingⓘ	no	87.38	yes
125438	aerobic	aerobicⓘ	yes	85.91	yes
125438	thermophilic	thermophileⓘ	no	93.96	yes
125438	flagellated	motile2+ⓘ	no	86.38	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Phylogenetic Relationships and Potential Functional Attributes of the Genus Parapedobacter: A Member of Family Sphingobacteriaceae.	Nagar S, Talwar C, Haider S, Puri A, Ponnusamy K, Gupta M, Sood U, Bajaj A, Lal R, Kumar R	Front Microbiol	10.3389/fmicb.2020.01725	2020
Phylogeny	Parapedobacter luteus sp. nov. and Parapedobacter composti sp. nov., isolated from cotton waste compost.	Kim SJ, Weon HY, Kim YS, Yoo SH, Kim BY, Anandham R, Kwon SW	Int J Syst Evol Microbiol	10.1099/ijs.0.013318-0	2009

References

#16611	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 22900
#20215	Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 )
#25856	IJSEM 1849 2010 ( DOI 10.1099/ijs.0.013318-0 , PubMed 19767359 )
#29451	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #25856
#66794	Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, Dietmar Schomburg: BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49: D498 - D508 2020 ( DOI 10.1093/nar/gkaa1025 , PubMed 33211880 )
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#69479	João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta .
#125438	Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 )
#125439	Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 .
#126262	A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 )

Content

About us

Parapedobacter composti (DSM 22900, JCM 15978, KACC 10972)

Name and taxonomic classification

Morphology

Cell morphology

Pigmentation

Culture and growth conditions

Culture medium

Culture temp

Culture pH

Physiology and metabolism

Oxygen tolerance

Spore formation

Halophily

Observation

Metabolite utilization

Enzymes

Pathway annotation

Isolation, sampling and environmental information

Isolation source categories

Isolation

Taxonmaps

Interaction and safety

Risk assessment

Sequence information

Genome sequences

Genome browser: GCA_900112315

16S sequences

GC content

Genome-based predictions

Predictions

Literature

Literature

References

BacDive

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